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47

I am going to recommend something that I have no doubt will get people completely up in arms and probably get people to attack me. It happened in the past and I lost many points on StackOverflow as people downvoted my answer. I certainly hope people are more open minded in the quant forum. Note - It seems that this suggestion has created some strong ...


31

Department of Mathematics at University of Minnesota has 4 online lectures on financial mathematics - Lectures on financial mathematics: Notes on Financial Mathematics The Risk-Neutral World Δ-Hedging The Central Limit Theorem David Harper aka Bionic Turtle has set of small videos on his website about quantitative finance and risk management - Bionic ...


25

quandl is a new data source for all kind of econometric time series.


18

This is a great question. I hope there are many valuable contributions. The recent (Jan 27, 28) MIT 150 Symposium, "Economics and Finance: From Theory to Practice to Policy". http://mit150.mit.edu/symposia/economics Specifically, the Jan 28 should be of interest (Finance). I particularly enjoyed Ross. "Finding Alpha" Videos (based on Falkenstein's Wiley ...


17

I don't know how interested you are in the CME data, but I have been learning about options and volatility modeling. I have been working with delayed CME data. I have been able to extract the JSON queries and now have been able to run them in my .NET application to get data for every asset type. Exmaple of ES options data: Run the query below in Chrome ...


15

Academic access to Thomson Reuters Tick History: www.sirca.org.au The Thomson Reuters Tick History database provides millisecond-timestamped tick data going back to January 1996, covering 45 million OTC and exchange-traded instruments worldwide. The database currently updates at a rate of 1 million messages per second and is around 3 Petabytes uncompressed....


13

Eric Zivot's Introduction to Computational Finance and Financial Econometrics on Coursera.


13

Accounting is a vital skill if you end up in a managerial position, and unless your career goal is to always be a cog in someone else's clockwork, then you will eventually find yourself in a managerial/senior partnership position even through quant research. I still play a critical role in my firm's quant strategies team, but here's a few things I've had to ...


12

Khan Academy now offers finance videos (he already started with e.g. the basics of option trading strategies and arbitrage pricing):


12

Google and Yahoo finance have a survivorship bias -- they only include firms that are still around. I know of no free source that provides the data you seek. I get my data from Compustat and CRSP via the Wharton Resource Data Service, but these (or Bloomberg or Reuters) are likely too expensive for an individual. Have you asked your broker if they will sell ...


11

I strongly recommend Robert Shiller's "Financial Markets".


11

There is certainly much more to quantitative finance than technical analysis, and a previous question does a decent job of outlining the different areas, as does the wikipedia on "quantitative analyst". Even for what wikipedia terms an "algorithmic trading quant" or what Mark Joshi terms a "statistical arbitrage quant", technical analysis is just one tool ...


11

All of the answers above (unfortunately highly upvoted at this point) are missing the point. You shouldn't pick a DBMS or storage solution by general performance benchmarks, you should pick it by use case. If someone says they get a "x ms read", "y inserts per second", "k times speedup", "store n TB data" or "have m years of experience" and use that to ...


11

Pull-to-par says that the bond's price will gradually converge toward par (100% of face value) when yield is unchanged. This process is also known as accretion for a bond trading at a discount (since its price gradually goes higher toward par) and amortization for a bond trading at a premium (since its price gradually declines toward par). Pull-to-par says ...


10

There is a mathfinance "tube": http://www.mathfinance.cn/video/


10

To get a consolidated feed of most of the data feeds here use Quandl. This is free for limited amount of requests per day.


10

This is the canonical Arrow-Pratt "portfolio" model. Couple of points on terminology: For a function $u$, we define the risk aversion function by $r_u(x):=-\frac{u''(x)}{u'(x)}$. In your utility function, $r_u(x) = \lambda$; hence, it is a constant absolute risk aversion utility and $\lambda$ is the "coefficient of risk aversion," not the "risk ...


10

The standard answer is going to be that for time series, you want a column store database. These are optimized for range queries (ie: give me everything between two timestamps) because crucially, they store data along one of the dimensions (which you must choose, usually time) contiguously on disk, and thus reads are extremely fast. The alternative, when ...


9

Somewhat more economic data can be found at e.g.: The World Bank The United Nations The OECD More financial: The IMF European Union / EFTA / EMU data: Eurostat European Central Bank (financial) Data from these sources is all freely available. You can also play with data from many of these sources using the Google Public Data Explorer.


9

Have a look here: http://www.climatelogic.com/ The method is based on a sequential F-test, see also this paper: Rodionov, S.N., 2005b: Detecting regime shifts in the mean and variance: Methods and specific examples. In: Large-Scale Disturbances (Regime Shifts) and Recovery in Aquatic Ecosystems: Challenges for Management Toward Sustainability, V. Velikova ...


9

If you want to learn more about price pressure, you should look after market impact of metaorders, which is a more adequate term. Because of the microstructure (i.e. the mix of orderbboks dynamics, trading rules, participants behaviours and habits, etc), the more you buy or sell, the more you influence the price an unfavorable way (for your trades). Just ...


9

There are a few things to consider: Price On average Thomson Reuters is known to be less costly than Bloomberg. One thing to consider when looking to save money is that most vendors will use some kind of ladder pricing. So if you cannot get rid of either Bloomberg or Thomson Reuters completely then you may not save as much as you expected. Technology ...


9

It is not financial mathematics in general, but a scientific approach that is beneficial: quantitative views and open objective tools make transactions more transparent. It decreases information asymmetry and thus decrease transaction costs in general (bid-ask spread, prices range, volatility, etc). thanks to (good) models, the consistency between ...


9

Pull-to-par just says that a bond's (clean) price will converge towards its face value as the bonds approaches maturity. There is nothing really interesting about pull-to-par - a bond's (clean) price has to converge to its face value, because a bond with just a few days to maturity is essentially a short-term cash deposit. Look at it this way - the price of ...


8

Quite a lot of lectures on Wilmott.com: http://wilmott.com/av.cfm


8

Quandl is a free one, with good economic and market data and an API http://www.quandl.com/


8

A couple of lecture note links, no video or audio, but these are pretty useful nonetheless. Notes from Emmanuel Derman's 2007 Columbia course on the Volatility Smile Andrew Lesniewski's 2009 notes on Interest Rate and Credit pricing, on his Lectures and Presentations page, there are a few other interesting presentations there as well.


8

It seems logical to me to have a Financial accounting course in a quant program. Quants can have a lot of different occupations, from derivative pricing to quant analyst in a "research" (i.e. analysis) dept. of a broker, a risk dept., a fund (as an analyst or as a potfolio manager), or quant execution trader (the list is far longer). In the case of being ...


8

Define excess return $r^x_{it} = r_{it} - r^f_{t}$ as the return $i$ minus the risk free rate, and $f_{jt}$ similarly denotes the excess return of factor $j$ at time $t$. Let's say we have some factor model of returns where: $$ r^x_{it} = \alpha_i + \sum_j \beta_{i,j} f_{jt} + \epsilon_{it}$$ F-test / GRS Test If we assume the error terms $\epsilon_{it}$ ...


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